SUACDNet: Attentional change detection network based on siamese U-shaped structure

Remote sensing image change detection is an essential aspect of remote sensing technology application. Existing change detection algorithms based on deep learning do not distinguish between changed and unchanged areas explicitly, resulting in serious loss of edge detail information during detection. Therefore, a new attentional change detection network based on Siamese U-shaped structure (SUACDNet) is proposed in this paper. In the feature encoding stage, three branches are introduced between the Siamese structure to focus on the global information, difference information and similarity information of bitemporal images respectively. In the feature decoding stage, an U-shaped structure is constructed for upsampling and recovery layer by layer. Multi-scale Convolution Residual Module (MCRM) is a new convolution structure designed for multi-scale feature extraction in the network. In addition, this work also proposes three auxiliary modules to optimize the network, namely Spatial Attention Module (SAM), Feature Fusion Module (FFM) and Cross-scale Global Context Semantic Information Aggregation Module (CGCAM), making the network more sensitive to the changed area while filtering out the background noise. Comparative experiments on three datasets show that our method is superior to the existing methods.